Following [1]}, existing methods can be generally categorized into three kinds: regularization, parameter isolation, and replay. Regularization-based methods aim to enforce parameter consistency during the training process by penalizing the changes of important parameters [2]}, [3]}, [4]}, [5]} or through knowledge distallation [6]}, [7]}. On the other hand, parameter-isolation methods assume that different subsets of the network parameters are attributed to different tasks, thus leading to a flexible gating mechanism. Isolated parameters for each sub-network can be achieved through a dynamically expanded network [8]}, [9]}, [10]}, [11]}, [12]} or through task-specific masking [13]}, [14]}, [15]}. Finally, replay-based methods store samples or generate pseudo samples as the memory of old knowledge. Special attention is devoted to different sample selection strategies [16]}, [17]}, sample generation strategies [18]}, [19]}, [20]}, and optimization constraints [21]}, [22]}. In this paper, we propose an experience replay approach to tackle the proposed continual neural mapping problem by leveraging past experience to guide the continual learning of new observations.
